U.S. patent number 10,977,871 [Application Number 15/962,090] was granted by the patent office on 2021-04-13 for delivery of a time-dependent virtual reality environment in a computing system.
This patent grant is currently assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION. The grantee listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to David Delia, Wayne M. Delia, Derek Difazio, Eric Lei.
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United States Patent |
10,977,871 |
Delia , et al. |
April 13, 2021 |
Delivery of a time-dependent virtual reality environment in a
computing system
Abstract
Embodiments for delivery of a time-dependent virtual reality
environment by a processor. A time-dependent three dimensional (3D)
virtual environment, having one or more configurable boundary
parameters, may be created according to user input, one or more
cognitive computing systems, data resources, or a combination
thereof.
Inventors: |
Delia; David (Lagrangeville,
NY), Delia; Wayne M. (Poughkeepsie, NY), Difazio;
Derek (Port Chester, NY), Lei; Eric (Poughkeepsie,
NY) |
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
INTERNATIONAL BUSINESS MACHINES
CORPORATION (Armonk, NY)
|
Family
ID: |
1000005486467 |
Appl.
No.: |
15/962,090 |
Filed: |
April 25, 2018 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20190333285 A1 |
Oct 31, 2019 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A63F
13/63 (20140902); G06T 19/20 (20130101); G06T
15/205 (20130101); A63F 2300/6018 (20130101) |
Current International
Class: |
G06T
19/20 (20110101); A63F 13/63 (20140101); G06T
15/20 (20110101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Donalek et al., "Immersive and Collaborative Data Visualization
Using Virtual Reality Platforms," IEEE International Conference on
Big Data, 2014 (6 pages). cited by applicant .
Olshannikova et al., "Visualizing Big Data with augmented and
virtual reality: challenges and research agenda," Journal of Big
Data, 2015 (27 pages). cited by applicant .
Moran et al., "Improving Big Data Visual Analytics with Interactive
Virtual Reality," IEEE High Performance Extreme Computing
Conference (HPEC), 2015 (6 pages). cited by applicant.
|
Primary Examiner: Wang; Samantha (Yuehan)
Attorney, Agent or Firm: Griffiths & Seaton PLLC
Claims
The invention claimed is:
1. A method, by a processor, for delivery of a time-dependent
virtual reality environment in a computing system, comprising:
receiving user input on a user interface of a query, wherein the
query includes one or more configurable boundary parameters, the
one or more boundary parameters including at least a selected time
period and a selected location geographically; responsive to
receiving the query, parsing and analyzing unstructured data in
data resources by one or more cognitive computing systems to
identify content within the historical documents, historical media
images, and other historical information associated with the
selected time period at the selected location, wherein the data
sources include governmental archives, historical news articles,
and other public archives, and wherein the content data sources is
cognitively deduced as relevant to the selected time period and the
selected location according to the analyzation by the cognitive
computing systems notwithstanding whether the content explicitly
references and includes data from the selected location at the
selected time period; amalgamating selected portions of the content
in the data sources deduced as relevant to the selected time period
and the selected location, wherein a natural language processing
(NLP) operation is used by the one or more cognitive computing
systems to transcribe text within the content in determining those
portions of the content are relevant to the selected location and
the selected time period; and creating a time-dependent three
dimensional (3D) virtual environment having the one or more
configurable boundary parameters using the selected portions of the
content.
2. The method of claim 1, further including: defining the one or
more configurable boundary parameters; or enhancing or adjusting
the one or more configurable boundary parameters according to the
selected time period, the selected location, the historical media
images, the user input, an analysis operation, the unstructured
data from the data resources, or a combination thereof.
3. The method of claim 1, wherein the one or more cognitive
computing systems include a machine learning system, an image and
audio recognition system, an intelligent search system, a cognitive
memory system, or combination thereof.
4. The method of claim 1, further including adding media data, the
user input, global positioning satellite (GPS) data, a plurality of
physical structure and landmark data, or a combination thereof to
enhance the time-dependent 3D virtual environment.
5. The method of claim 1, further including: collecting feedback
information from a user relating to the one or more configurable
boundary parameters; and initializing a machine learning component
to learn, adjust, or update the one or more configurable boundary
parameters according to the feedback information.
6. The method of claim 1, further including providing the
time-dependent 3D virtual environment via the user interface of at
least one of one or more Internet of Things (IoT) devices in an IoT
computing network or a 3D video gaming system.
7. A system for delivery of a time-dependent virtual reality
environment in a computing system, comprising: one or more
computers with executable instructions that when executed cause the
system to: receive user input on a user interface of a query,
wherein the query includes one or more configurable boundary
parameters, the one or more boundary parameters including at least
a selected time period and a selected location geographically;
responsive to receiving the query, parse and analyze unstructured
data in data resources by one or more cognitive computing systems
to identify content within the historical documents, historical
media images, and other historical information associated with the
selected time period at the selected location, wherein the data
sources include governmental archives, historical news articles,
and other public archives, and wherein the content data sources is
cognitively deduced as relevant to the selected time period and the
selected location according to the analyzation by the cognitive
computing systems notwithstanding whether the content explicitly
references and includes data from the selected location at the
selected time period; amalgamate selected portions of the content
in the data sources deduced as relevant to the selected time period
and the selected location, wherein a natural language processing
(NLP) operation is used by the one or more cognitive computing
systems to transcribe text within the content in determining those
portions of the content are relevant to the selected location and
the selected time period; and create a time-dependent three
dimensional (3D) virtual environment having the one or more
configurable boundary parameters using the selected portions of the
content.
8. The system of claim 7, wherein the executable instructions that
when executed cause the system to: define the one or more
configurable boundary parameters; or enhance or adjust the one or
more configurable boundary parameters according to the selected
time period, the selected location, the historical media images,
the user input, an analysis operation, the unstructured data from
the data resources, or a combination thereof.
9. The system of claim 7, wherein the one or more cognitive
computing systems include a machine learning system, an image and
audio recognition system, an intelligent search system, a cognitive
memory system, or combination thereof.
10. The system of claim 7, wherein the executable instructions that
when executed cause the system to add media data, the user input,
global positioning satellite (GPS) data, a plurality of physical
structure and landmark data, or a combination thereof to enhance
the time-dependent 3D virtual environment.
11. The system of claim 7, wherein the executable instructions that
when executed cause the system to: collect feedback information
from a user relating to the one or more configurable boundary
parameters; and initialize a machine learning component to learn,
adjust, or update the one or more configurable boundary parameters
according to the feedback information.
12. The system of claim 7, wherein the executable instructions that
when executed cause the system to provide the time-dependent 3D
virtual environment via the user interface of at least one of one
or more Internet of Things (IoT) devices in an IoT computing
network or a 3D video gaming system.
13. A computer program product for, by a processor, delivery of a
time-dependent virtual reality environment in a computing system,
the computer program product comprising a non-transitory
computer-readable storage medium having computer-readable program
code portions stored therein, the computer-readable program code
portions comprising: an executable portion that receives user input
on a user interface of a query, wherein the query includes one or
more configurable boundary parameters, the one or more boundary
parameters including at least a selected time period and a selected
location geographically; an executable portion that, responsive to
receiving the query, parses and analyzes unstructured data in data
resources by one or more cognitive computing systems to identify
content within the historical documents, historical media images,
and other historical information associated with the selected time
period at the selected location, wherein the data sources include
governmental archives, historical news articles, and other public
archives, and wherein the content data sources is cognitively
deduced as relevant to the selected time period and the selected
location according to the analyzation by the cognitive computing
systems notwithstanding whether the content explicitly references
and includes data from the selected location at the selected time
period; an executable portion that amalgamates selected portions of
the content in the data sources deduced as relevant to the selected
time period and the selected location, wherein a natural language
processing (NLP) operation is used by the one or more cognitive
computing systems to transcribe text within the content in
determining those portions of the content are relevant to the
selected location and the selected time period; and an executable
portion that creates a time-dependent three dimensional (3D)
virtual environment having the one or more configurable boundary
parameters using the selected portions of the content.
14. The computer program product of claim 13, further including an
executable portion that: defines the one or more configurable
boundary parameters; adjusts the one or more configurable boundary
parameters according to the selected time period, the selected
location, the historical media images, the user input, an analysis
operation, the unstructured data from the data resources, or a
combination thereof; or enhances the time-dependent 3D virtual
environment by adding the one or more media images, updated user
input, global positioning satellite (GPS) data, a plurality of
physical structure and landmark data, or a combination thereof.
15. The computer program product of claim 13, wherein the one or
more cognitive computing systems include a machine learning system,
an image and audio recognition system, an intelligent search
system, a cognitive memory system, or combination thereof.
16. The computer program product of claim 13, further including an
executable portion that: collects feedback information from a user
relating to the one or more configurable boundary parameters; and
initializes a machine learning component to learn, adjust, or
update the one or more configurable boundary parameters according
to the feedback information.
17. The computer program product of claim 13, further including an
executable portion that provides the time-dependent 3D virtual
environment via the user interface of at least one of one or more
Internet of Things (IoT) devices in an IoT computing network or a
3D video gaming system.
Description
BACKGROUND OF THE INVENTION
Field of the Invention
The present invention relates in general to computing systems, and
more particularly, to various embodiments for delivery of a
time-dependent virtual reality environment in a computing system
using one or more computing processors.
Description of the Related Art
In today's society, computer systems are commonplace. Computer
systems may be found in the workplace, at home, or at school.
Computer systems may include data storage systems, or disk storage
systems, to process and store data. In recent years, both software
and hardware technologies have experienced amazing advancement.
With the new technology, more and more functions are added, and
greater convenience is provided for use with these computing
systems. For example, technological advances enable computer
systems such as, for example, console gaming systems, to grow in
popularity with a wide array of functions and features.
SUMMARY OF THE INVENTION
Various embodiments for delivery of a time-dependent virtual
reality environment in a computing system by a processor are
provided. In one embodiment, by way of example only, a method for
delivery of a time-dependent virtual reality environment, again by
a processor, is provided. A time-dependent three dimensional (3D)
virtual environment, having one or more configurable boundary
parameters, may be created according to user input, one or more
cognitive computing systems, data resources, or a combination
thereof.
BRIEF DESCRIPTION OF THE DRAWINGS
In order that the advantages of the invention will be readily
understood, a more particular description of the invention briefly
described above will be rendered by reference to specific
embodiments that are illustrated in the appended drawings.
Understanding that these drawings depict only typical embodiments
of the invention and are not therefore to be considered to be
limiting of its scope, the invention will be described and
explained with additional specificity and detail through the use of
the accompanying drawings, in which:
FIG. 1 is a block diagram depicting an exemplary cloud computing
node according to an embodiment of the present invention;
FIG. 2 is an additional block diagram depicting an exemplary cloud
computing environment according to an embodiment of the present
invention;
FIG. 3 is an additional block diagram depicting abstraction model
layers according to an embodiment of the present invention;
FIG. 4 is a diagram depicting various user hardware and computing
components functioning in accordance with aspects of the present
invention;
FIG. 5 is a block diagram depicting a system for delivery of a
time-dependent virtual reality environment by a processor, again in
which aspects of the present invention may be realized;
FIGS. 6A-6B are diagrams depicting a time-dependent 3D virtual
reality environment in a computing system in accordance with
aspects of the present invention;
FIG. 7 is a flowchart diagram depicting an exemplary method for
delivery of a time-dependent 3D virtual reality environment in a
computing system by a processor in accordance with aspects of the
present invention; and
FIG. 8 is a flowchart diagram depicting an exemplary method for
delivery of a time-dependent 3D virtual reality environment in a
computing system by a processor, again in which aspects of the
present invention may be realized.
DETAILED DESCRIPTION OF THE DRAWINGS
Various embodiments relate to the field of cognitive systems with
respect to Big Data application and virtual reality simulation. Big
Data is a collection of tools, techniques, and operations used for
data sets that becomes so voluminous and complex that traditional
data processing applications are inadequate to store, query,
analyze or process the data sets using current database management
and data warehousing tools or traditional data processing
applications. As computer and console gaming continue to grow in
popularity and newer virtual reality technologies continue to gain
traction, the virtual environments utilized by various games and
computing systems are growing in significance. For example, the
physical landscape of various games, mapping systems, and/or
computing systems are vital to the impact of the functionality and
features (e.g., for actual gameplay) similar to how the location of
a film can add new dimensions to a motion picture.
Accordingly, a need exists to provide a solution that leverages
unstructured data from available Big Data sources to enable time
and/or era specific simulation for a three dimensional (3D) virtual
environment. In one aspect, the present invention provides a
cognitive system for the digital creation of 3D virtual
environments that emulates specific past dates or time-periods for
specific locations. One or more big data sources may be used for 3D
virtual environments creation. Also, additional non-conventional
data sources may also be used to enhance, refine, and/or alter a
simulated environment and/or the time/era specific emulation. A
user may provide user input to the user's customized 3D virtual
environment (e.g., a 3D virtual gaming environment) by selecting
both time-period details and location data.
It should be noted as described herein, the term "cognitive" (or
"cognition") may be relating to, being, or involving conscious
intellectual activity such as, for example, thinking, reasoning, or
remembering, that may be performed using a machine learning. In an
additional aspect, cognitive or "cognition" may be the mental
process of knowing, including aspects such as awareness,
perception, reasoning and judgment. A machine learning system may
use artificial reasoning to interpret data from one or more data
sources and learn topics, concepts, and/or processes that may be
determined and/or derived by machine learning.
In an additional aspect, cognitive or "cognition" may refer to a
mental action or process of acquiring knowledge and understanding
through thought, experience, and one or more senses using machine
learning (which may include using sensor based devices or other
computing systems that include audio or video devices). Cognitive
may also refer to identifying patterns of behavior, leading to a
"learning" of one or more events, operations, or processes. Thus,
the cognitive model may, over time, develop semantic labels to
apply to observed behavior and use a knowledge domain or ontology
to store the learned observed behavior. In one embodiment, the
system provides for progressive levels of complexity in what may be
learned from the one or more events, operations, or processes.
In an additional aspect, the term cognitive may refer to a
cognitive system. The cognitive system may be a specialized
computer system, or set of computer systems, configured with
hardware and/or software logic (in combination with hardware logic
upon which the software executes) to emulate human cognitive
functions. These cognitive systems apply human-like characteristics
to convey and manipulate ideas which, when combined with the
inherent strengths of digital computing, can solve problems with a
high degree of accuracy (e.g., within a defined percentage range or
above an accuracy threshold) and resilience on a large scale. A
cognitive system may perform one or more computer-implemented
cognitive operations that approximate a human thought process while
enabling a user or a computing system to interact in a more natural
manner. A cognitive system may comprise artificial intelligence
logic, such as natural language processing (NLP) based logic, for
example, and machine learning logic, which may be provided as
specialized hardware, software executed on hardware, or any
combination of specialized hardware and software executed on
hardware. The logic of the cognitive system may implement the
cognitive operation(s), examples of which include, but are not
limited to, question answering, identification of related concepts
within different portions of content in a corpus, and intelligent
search algorithms, such as Internet web page searches.
In general, such cognitive systems are able to perform the
following functions: 1) Navigate the complexities of human language
and understanding; 2) Ingest and process vast amounts of structured
and unstructured data; 3) Generate and evaluate hypotheses; 4)
Weigh and evaluate responses that are based only on relevant
evidence; 5) Provide situation-specific advice, insights,
estimations, determinations, evaluations, calculations, and
guidance; 6) Improve knowledge and learn with each iteration and
interaction through machine learning processes; 7) Enable decision
making at the point of impact (contextual guidance); 8) Scale in
proportion to a task, process, or operation; 9) Extend and magnify
human expertise and cognition; 10) Identify resonating, human-like
attributes and traits from natural language; 11) Deduce various
language specific or agnostic attributes from natural language; 12)
Memorize and recall relevant data points (images, text, voice)
(e.g., a high degree of relevant recollection from data points
(images, text, voice) (memorization and recall)); and/or 13)
Predict and sense with situational awareness operations that mimic
human cognition based on experiences.
It is understood in advance that although this disclosure includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud-computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 1, a schematic of an example of a cloud
computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
In cloud computing node 10 there is a computer system/server 12,
which is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context
of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
As shown in FIG. 1, computer system/server 12 in cloud computing
node 10 is shown in the form of a general-purpose computing device.
The components of computer system/server 12 may include, but are
not limited to, one or more processors or processing units 16, a
system memory 28, and a bus 18 that couples various system
components including system memory 28 to processor 12.
Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
Computer system/server 12 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system/server 12, and it includes both
volatile and non-volatile media, removable and non-removable
media.
System memory 28 can include computer system readable media in the
form of volatile memory, such as random access memory (RAM) 30
and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
system memory 28 may include at least one program product having a
set (e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules
42, may be stored in system memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more
external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
Referring now to FIG. 2, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 comprises
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or video gaming system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 2 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 3, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 2) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 3 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Device layer 55 includes physical and/or virtual devices, embedded
with and/or standalone electronics, sensors, actuators, and other
objects to perform various tasks in a cloud computing environment
50. Each of the devices in the device layer 55 incorporates
networking capability to other functional abstraction layers such
that information obtained from the devices may be provided thereto,
and/or information from the other abstraction layers may be
provided to the devices. In one embodiment, the various devices
inclusive of the device layer 55 may incorporate a network of
entities collectively known as the "internet of things" (IoT). Such
a network of entities allows for intercommunication, collection,
and dissemination of data to accomplish a great variety of
purposes, as one of ordinary skill in the art will appreciate.
Device layer 55 as shown includes sensor 52, actuator 53,
"learning" thermostat 56 with integrated processing, sensor, and
networking electronics, camera 57, controllable household
outlet/receptacle 58, and controllable electrical switch 59 as
shown. Other possible devices may include, but are not limited to
various additional sensor devices, networking devices, electronics
devices (such as a remote control device), additional actuator
devices, so called "smart" appliances such as a refrigerator or
washer/dryer, and a wide variety of other possible interconnected
objects.
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 82 provides cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provides pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and, in the
context of the illustrated embodiments of the present invention,
various consistent data replication workloads and functions 96. In
addition, data replication workloads and functions 96 may include
such operations as data analytics, data analysis, and as will be
further described, notification functionality. One of ordinary
skill in the art will appreciate that the consistent data
replication workloads and functions 96 may also work in conjunction
with other portions of the various abstractions layers, such as
those in hardware and software 60, virtualization 70, management
80, and other workloads 90 (such as data analytics processing 94,
for example) to accomplish the various purposes of the illustrated
embodiments of the present invention.
In one aspect, a time-dependent three dimensional (3D) virtual
environment, having one or more configurable boundary parameters,
may be created according to user input, one or more cognitive
computing systems, data resources, or a combination thereof.
Turning now to FIG. 4, a block diagram of exemplary functionality
400 relating to extraction and summarization of decision
discussions is depicted. As shown, the various blocks of
functionality are depicted with arrows designating the blocks' 400
relationships with each other and to show process flow.
Additionally, descriptive information is also seen relating each of
the functional blocks 400. As will be seen, many of the functional
blocks may also be considered "modules" of functionality, in the
same descriptive sense as has been previously described in FIGS.
1-3. With the foregoing in mind, the module blocks 400 may also be
incorporated into various hardware and software components of a
system for extraction and summarization of decision methods and
features in accordance with the present invention, such as those
described in FIGS. 1-3. Many of the functional blocks 400 may
execute as background processes on various components, either in
distributed computing components, or on the user device, or
elsewhere.
Multiple data sources 401-404 may be provided by one or more data
resources (e.g., cloud computing services, Big Data resources such
as, for example, a distributed file system i.e., a Hadoop file
system ("HDFS")). The data sources 401-404 may be provided as a
corpus or group of data sources defined and/or identified. The data
sources 401-404 may include, but are not limited to, data sources
relating to one or more documents, historical records, government
records, newspaper articles and images, mapping and geographical
records and data, structural data (e.g., buildings, landmark,
etc.), musical archive data, books, scientific papers, online
journals, journals, articles, drafts, materials related to emails,
audio data, images or photographs, video data, and/or other various
documents or data sources capable of being analyzed, published,
displayed, interpreted, transcribed, or reduced to text data. The
data sources 401-404 may be all of the same type, for example,
pages or articles in a wiki or pages of a blog. Alternatively, the
data sources 401-404 may be of different types, such as word
documents, wikis, web pages, power points, printable document
format, or any document capable of being analyzed by a natural
language processing system.
In addition to text-based documents, other data sources such as
audio, video or image sources may also be used wherein the audio,
video or image sources may be pre-analyzed to extract or transcribe
their content for natural language processing, such as converting
from image to text, text to image, or visual recognition and
analysis. For example, a photograph combined with a newspaper
article and mapping data (e.g., global positioning satellite
("GPS") data) may be analyzed for creating a 3D virtual
representation of a particular location at a selected time (e.g.,
creating a 3D virtual representation of a city park in 1930 based
on the photographs, newspaper articles, and mapping data). As an
additional example, a media capturing device 404 (e.g., a camera)
may have captured a photograph of a selected time period such as,
for example, an aerial image/photograph of a city landscape in
1945. The image data captured by the media capturing device 404 may
be analyzed and used to recreate a 3D virtual representation of a
particular location at a selected time as compared to the same,
current city landscape. The group of data sources 401-404 are
consumed for an extraction, analysis, and processing for creating
the 3D virtual representation of a particular location at a
selected time using natural language processing (NLP) and
artificial intelligence (AI) to create the time-dependent virtual
reality environment system 430.
The data sources 401-404 may be analyzed by an NLP component 410
(and a time and location component 435 if necessary) to data mine,
analyze image data, transcribe relevant information from the
content of the data sources 401-404 (e.g., documents, emails,
reports, notes, records, maps, images, video recordings,
live-streaming communications, etc.) in order to create the 3D
virtual representation and/or provide the information in a more
searchable and displayable manner. The NLP component 410 may be
provided as a cloud service or as a local service.
The time-dependent virtual reality environment system 430 may
include the NLP component 410, a content consuming component 411, a
characteristics association component 412, and an analytics
component 450. The NLP component 410 may be associated with the
content consuming component 411. The content consuming component
411 may be used for inputting the data sources 401-404 and running
NLP and AI tools against them, learning the content, such as by
using the machine learning component 438. It should be noted that
other components of FIG. 4 may also employ one or more NLP systems
and the NLP component 410 and is merely illustrated by way of
example only of use of an NLP system. As the NLP component 410
(including the machine learning component 438) learns different
sets of data (e.g., images, maps, landscapes, historical
information, etc.), the characteristics association component 412
(or "cognitive characteristics association component") may use the
artificial intelligence to make cognitive associations or links
between data sources 401-404 by determining images, landmarks,
events, activities, historical data, structures, concepts, methods,
similar characteristics, underlying common topics, and/or
features.
Cognition is the mental process of knowing, including aspects such
as awareness, perception, reasoning and judgment. An AI system uses
artificial reasoning to interpret the data sources 401-404 and
extract their topics, ideas, or concepts. The learned decisions,
decision elements, alternatives to the decision, alternative
options/choices, decision criteria, concepts, suggestions, topics
and subtopics of a domain of interest, may not be specifically
named or mentioned in the data sources 401-404 and is derived or
inferred by the AI interpretation.
The learned content of the data sources consumed by the NLP system
may be merged into a database 420 (and/or knowledge store) or other
data storage method of the consumed content with learned images,
landmarks, events, activities, historical data, structures,
concepts, methods, similar characteristics, underlying common
topics, and/or features of the data sources 401-404 providing
association between the content referenced to the original data
sources 401-404.
The database 420 may record and maintain the evolution of cognitive
decisions, alternatives, criteria, subjects, topics, ideas, or
content discussed in the data sources 401-404. The database 420 may
track, identify, and associate all communication threads, messages,
transcripts, images, mapping and geographical records and data,
structural data (e.g., buildings, landmarks, etc.), musical archive
data, books, scientific papers, online journals, journals,
articles, drafts, materials related to emails, audio data, images
or photographs, video data, and/or other various documents of all
data generated during all stages of the development or "life cycle"
of the decisions, decision elements, alternatives, choices,
criteria, subjects, topics, or ideas. The merging of the data into
one database 420 (which may include a domain knowledge) allows the
time-dependent virtual reality environment system 430 to act like a
search engine, but instead of keyword searches, it will use an AI
method of making cognitive associations between the data sources
using the deduced concepts so as to create a time-dependent 3D
virtual reality environment according to time, space, and
location.
The time-dependent virtual reality environment system 430 may
include a user interface ("UP") component 434 (e.g., an interactive
graphical user interface "GUI") for providing user interaction for
sending or receiving one or more inputs/queries from a user. More
specifically, the user interface component 434 may be in
communication with a wireless communication device 455 (see also
the PDA or cellular telephone 54A, the desktop computer 54B, the
laptop computer 54C, and/or the video gaming system 54N of FIG. 2.)
for also providing user input for inputting data such as, for
example, data sources 401-404 and also providing user interaction
for defining a selected time, selected location, one or more
configurable boundaries and/or providing input for enhancing or
adjusting the one or more configurable boundary parameters
according to a selected time period, a selected location, one or
more media images, the user input, an analysis operation,
unstructured data from the data resources, or a combination thereof
to create a 3D time-dependent virtual reality. The computing device
455 may use the UI component 434 (e.g., GUI) for providing input of
data and/or providing a query functionality such as, for example,
interactive GUI functionality for enabling a user to enter a query
in the GUI 422 relating to the selected time, the selected
location, one or more configurable boundaries, and/or other
parameters, domain of interest, topic, decision, alternative
criteria, or additional analysis. For example, GUI 422 may display
3D time-dependent virtual reality created according to the selected
time, the selected location, and/or one or more configurable
boundaries.
The time-dependent virtual reality environment system 430 may also
include a 3D virtual reality component 432. The 3D virtual reality
component 432 may use data retrieved directly from one or more data
sources 401-404, data stored in the database 420 (or multiple
immutable ledgers), data received from the user via the computing
device 455, data from the time and location component 435 (e.g., a
GPS device, mapping service, etc.), other components and/or a
combination thereof to create a time-dependent three dimensional
(3D) virtual environment having one or more configurable boundary
parameters. That is, the 3D virtual reality component 432 may
create and generate a 3D virtual reality based on user input, one
or more cognitive computing systems (e.g., image/audio recognition
component 437, machine learning component 438, analytics component
450, NLP component 410, etc.), data resources (e.g., data sources
401-404 received from data sources such as, for example, an HDFS or
cloud computing system), or a combination thereof. The 3D virtual
reality component 432 may also enhance, enrich and/or adjust the
one or more configurable boundary parameters of the 3D virtual
reality representation according to the selected time period, the
selected location, one or more media images, the user input, an
analysis operation, unstructured data from the data resources, or a
combination thereof. In one aspect, for example, once the NLP
component 410 has carried out the linking of the data, the 3D
virtual reality component 432 may mine the maps, images, user
input, time data, location data, concepts, topics, or similar
characteristics, each of which may be stored in, and retrieved
from, the database 420 of the consumed content to create the
time-dependent 3D virtual reality presentation.
The time-dependent virtual reality environment system 430 may also
include an image/audio recognition component 437 for identifying,
updating, and/or enhancing media data (e.g., images, photographs,
videos, audio data, live streaming data, etc.) and/or providing
information relating to the plurality of images, photographs,
videos, audio data, live streaming data according to a domain
knowledge, which may be included in the database 420 and/or
associated with the database 420. That is, the image/audio
recognition component 437 may use one or more deep learning
operations to analyze images for scenes, objects, faces, colors,
food, text, explicit content and other subjects, and to understand
the contents of images. The image/audio recognition component 437
may also enhance and classify the images, photographs, videos,
audio data, and live streaming data.
The time-dependent virtual reality environment system 430 may
include a mapping component 436. The mapping component 436 may
provide mapping data relating to one or more time and space
locations for various periods of time and locations. For example,
the mapping component 436 may include topographical maps, aerial
maps, electronic maps, video gaming landscapes, features, and
design data, one or more GPS navigational tools/maps,
user-selectable maps, historical maps, governmental maps, landmark
data, survey data, construction maps/data, building blueprints
data, roadmaps, or other geographical information or data relating
to time or space.
The time-dependent virtual reality environment system 430 may
include an analytics component 450 that may be used to analyze
media data, user input, and unstructured data from the data sources
401-404 (e.g., received from various data resources) using the one
or more cognitive computing applications.
A feedback component 439 may also be included in the time-dependent
virtual reality environment system 430. For example, the feedback
component 439 may collect feedback information from a user relating
to the one or more configurable boundary parameters.
The time-dependent virtual reality environment system 430 may also
include a machine learning component 438. The machine learning
component 438 may learn, adjust, teach, or update the one or more
configurable boundary parameters for creating, enhancing, and/or
updating the time-dependent 3D virtual reality environment
according to the feedback information. The machine learning
component 438 may apply one or more heuristics and machine learning
based models using a wide variety of combinations of methods, such
as supervised learning, unsupervised learning, temporal difference
learning, reinforcement learning and so forth. Some non-limiting
examples of supervised learning which may be used with the present
technology include AODE (averaged one-dependence estimators),
artificial neural networks, Bayesian statistics, naive Bayes
classifier, Bayesian network, case-based reasoning, decision trees,
inductive logic programming, Gaussian process regression, gene
expression programming, group method of data handling (GMDH),
learning automata, learning vector quantization, minimum message
length (decision trees, decision graphs, etc.), lazy learning,
instance-based learning, nearest neighbor algorithm, analogical
modeling, probably approximately correct (PAC) learning, ripple
down rules, a knowledge acquisition methodology, symbolic machine
learning algorithms, sub symbolic machine learning algorithms,
support vector machines, random forests, ensembles of classifiers,
bootstrap aggregating (bagging), boosting (meta-algorithm), ordinal
classification, regression analysis, information fuzzy networks
(IFN), statistical classification, linear classifiers, fisher's
linear discriminant, logistic regression, perceptron, support
vector machines, quadratic classifiers, k-nearest neighbor, hidden
Markov models and boosting. Some non-limiting examples of
unsupervised learning which may be used with the present technology
include artificial neural network, data clustering,
expectation-maximization, self-organizing map, radial basis
function network, vector quantization, generative topographic map,
information bottleneck method, IBSEAD (distributed autonomous
entity systems based interaction), association rule learning,
apriori algorithm, eclat algorithm, FP-growth algorithm,
hierarchical clustering, single-linkage clustering, conceptual
clustering, partitional clustering, k-means algorithm, fuzzy
clustering, and reinforcement learning. Some non-limiting examples
of temporal difference learning may include Q-learning and learning
automata. Specific details regarding any of the examples of
supervised, unsupervised, temporal difference or other machine
learning described in this paragraph are known and are considered
to be within the scope of this disclosure.
In one aspect, the domain knowledge may be an ontology of concepts
representing a domain of knowledge. A thesaurus or ontology may be
used as the domain knowledge and may also be used to identify
semantic relationships between observed and/or unobserved
variables. In one aspect, the term "domain" is a term intended to
have its ordinary meaning. In addition, the term "domain" may
include an area of expertise for a system or a collection of
material, information, content and/or other resources related to a
particular subject or subjects. A domain can refer to information
related to any particular subject matter or a combination of
selected subjects.
The term ontology is also a term intended to have its ordinary
meaning. In one aspect, the term ontology in its broadest sense may
include anything that can be modeled as an ontology, including but
not limited to, taxonomies, thesauri, vocabularies, and the like.
For example, an ontology may include information or content
relevant to a domain of interest or content of a particular class
or concept. The ontology can be continuously updated with the
information synchronized with the sources, adding information from
the sources to the ontology as models, attributes of models, or
associations between models within the ontology.
Additionally, the domain knowledge may include one or more external
resources such as, for example, links to one or more Internet
domains, webpages, and the like. For example, text data may be
hyperlinked to a webpage that may describe, explain, or provide
additional information relating to the text data. Thus, a summary
may be enhanced via links to external resources that further
explain, instruct, illustrate, provide context, and/or additional
information to support a decision, alternative suggestion,
alternative choice, and/or criteria.
In one aspect, the time-dependent virtual reality environment
system 430 may perform one or more various types of calculations or
computations. The calculation or computation operations may be
performed using various mathematical operations or functions that
may involve one or more mathematical operations (e.g., solving
differential equations or partial differential equations
analytically or computationally, using addition, subtraction,
division, multiplication, standard deviations, means, averages,
percentages, statistical modeling using statistical distributions,
by finding minimums, maximums or similar thresholds for combined
variables, etc.). It should be noted that each of the components of
the time-dependent virtual reality environment system 430 may be
individual components and/or separate components of the
time-dependent virtual reality environment system 430.
In view of the method 400 of FIG. 4, FIG. 5 depicts an additional
system architecture of a cognitive time-dependent virtual reality
environment system. In one aspect, one or more of the components,
modules, services, applications, and/or functions described in
FIGS. 1-4 may be used in FIG. 5.
The cognitive time-dependent virtual reality environment system 500
may include a user 502, a media device 504, a location system 506
(e.g., GPS system), one or more data resources 510, which may be
associated with a cloud computing system 508 (or distributed file
system), and a cognitive time-dependent 3D virtual reality creation
system 550. In one aspect, the cognitive time-dependent virtual
reality environment system may employ one or more cognitive
applications (e.g., NLP, artificial intelligence (AI), machine
learning, IBM.RTM. Watson.RTM. Alchemy Language (IBM Watson and
Alchemy are trademarks of International Business Machines
Corporation)) and one or more data resources 510 (e.g., Big Data
resources such as data from a HDFS, GPS Satellite imaging data,
cloud computing data, etc.), combined with user input to create a
time-dependent 3D virtual reality environment 512 that are time
period specific and location specific. The time-dependent 3D
virtual reality environment 512 may be used in a variety of
computing applications and systems such as, for example, using the
time-dependent 3D virtual reality environment 512 in video
entertainment products, forensic investigation recreation systems,
and the like.
In one aspect, the cognitive time-dependent 3D virtual reality
creation system 550 may receive one or more various inputs for
creating the time-dependent 3D virtual reality environment. For
example, a user 502 may provide one or more configurable boundary
parameters to the cognitive time-dependent 3D virtual reality
creation system 550. Media data (e.g., photographs, videos, etc.)
may be input into the cognitive time-dependent 3D virtual reality
creation system 550 such as, for example, scanned photographs to
teach and refine the cognitive time-dependent 3D virtual reality
creation system 550. Location data from location system 506 (e.g.,
GPS satellite data, or other type of mapping or location system)
may provide input location data pertaining to a selected location
(e.g., input an image of an aerial photograph captured over a
selected area at a selected time period). Additionally, one or more
data resources 510 may provide various types of data to the
cognitive time-dependent 3D virtual reality creation system 550
such as, for example, unstructured data that may be in the form of
government historical records, newspaper articles and images,
weather information, musical archives and historical manufacturing
archives (such as automotive and technological data), to
supplement, enhance and refine the bounded simulation environment
for both time period and location.
The cognitive time-dependent 3D virtual reality creation system
550, using the various input data (e.g., the configurable boundary
parameters, image data, unstructured data, etc.), may be used to
define a selected location, selected span/universe (e.g., size and
scope of the virtual reality representation), and selected time
period. The cognitive time-dependent 3D virtual reality creation
system 550 may then use the unstructured data to generate and
create a 3D virtual reality representation. One or more added
images and details (and/or updated user input, unstructured data,
or location data) may be used to teach the cognitive time-dependent
3D virtual reality creation system 550 to refine, adjust, and
enhance the time-dependent 3D virtual reality environment 512.
In an additional aspect, the user 502 may define (e.g., the
location, the size/scope, time period) and refine the
time-dependent 3D virtual reality environment 512 by enabling the
use of personal photographs and input of specific parameters for
structures, such as buildings and landmarks. The time-dependent 3D
virtual reality environment 512 (e.g., the output of the
time-dependent 3D virtual reality creation system 550) may be a 3D
virtual environment that is bounded, defined, refined and taught by
the user of the time-dependent 3D virtual reality creation system
550, which can be integrated into various computing systems or
applications such as, for example, 3D gaming systems for being
time, era/period, and location specific or forensic investigations
of cold cases from specific time periods.
Turning now to FIGS. 6A-6B, diagrams depict a time-dependent 3D
virtual reality environment 600 in accordance with aspects of the
present invention. In one aspect, one or more of the components,
modules, services, applications, and/or functions described in
FIGS. 1-5 may be used in FIGS. 6A-6B. Diagram 600 illustrates a
present day map 610 (e.g., an aerial photograph of the present day
of a selected location and a time-dependent 3D virtual reality
environment 620 having one or more configurable boundary parameters
according to user input, one or more cognitive computing systems,
data resources, or a combination thereof. To further illustrate,
assume a user desires to see a photographic rendering of the time
period 1965 that includes a stadium (e.g., stadium 3) where a music
concert was held. Assume, also that stadium 3 was subsequently
demolished and is now a parking lot. However, the current image 610
displays a photographic rendering of the present day only showing
stadium 1 and a parking lot (which was were stadium 3 was
previously located) and stadium 2.
Thus, using the various embodiments described herein, the
mechanisms of the present invention provide a solution for delivery
of a time-dependent virtual reality environment. That is, using the
configurable boundary parameters defining a selected time, space,
and location (e.g., a photographic rendering of the time period
1965 that includes a stadium 3 where a music concert was held), a
time-dependent 3D virtual environment is created of 1965 having one
or more configurable boundary parameters illustrating stadium 3
according to the user input, one or more cognitive computing
systems, data resources, or a combination thereof. As depicted,
stadium 3 is now depicted in the time-dependent 3D virtual reality
environment 620 in the original position of the selected location
and the selected time period of 1965.
FIG. 7 is a flowchart diagram depicting an exemplary method for
delivery of a time-dependent virtual reality environment in a
computing environment. The functionality 700 may be implemented as
a method executed as instructions on a machine, where the
instructions are included on at least one computer readable medium
or one non-transitory machine-readable storage medium. The
functionality 700 may start in block 702. A time-dependent three
dimensional (3D) virtual environment, having one or more
configurable boundary parameters, may be created according to user
input, one or more cognitive computing systems, data resources, or
a combination thereof, as in block 704. The functionality 700 may
end, as in block 706.
FIG. 8 is a flowchart diagram depicting an exemplary method for
data replication in a distributed file system environment. The
functionality 800 may be implemented as a method executed as
instructions on a machine, where the instructions are included on
at least one computer readable medium or one non-transitory
machine-readable storage medium. The functionality 800 may start in
block 802. A time period and a location may be selected for
creating a time-dependent three dimensional (3D) virtual
environment, as in block 804. A 3D virtual environment, having one
or more configurable boundary parameters based upon the selected
time period and selected location, may be created according to user
input data, one or more cognitive computing systems, one or more
data resources, or a combination thereof, as in block 806. The 3D
virtual environment may be provided via an interface (GUI) of at
least one of the one or more IoT devices in an IoT computing
network or a 3D video gaming system, as in block 808. The
functionality 800 may end, as in block 810.
In one aspect, in conjunction with and/or as part of at least one
block of FIGS. 7-8, the operations of methods 700 and/or 800 may
include each of the following. The operations of methods 700 and/or
800 may select a time period and a location for creating the
time-dependent 3D virtual environment. The one or more configurable
boundary parameters may be defined. The one or more configurable
boundary parameters may be enhanced, enriched, and/or adjusted
according to a selected time period, a selected location, one or
more media images, the user input, an analysis operation,
unstructured data from the data resources, or a combination
thereof.
The operations of methods 700 and/or 800 may analyze media data,
the user input, unstructured data from the data resources using the
one or more cognitive computing systems, wherein the one or more
cognitive computing systems include a machine learning system, a
natural langue processing (NLP) system, an image and audio
recognition system, an intelligent search system, a cognitive
memory system, or combination thereof. Media data, additional user
input data, global positioning satellite (GPS) data, a plurality of
physical structure and landmark data may be added to enhance,
enrich, and/or adjust the time-dependent 3D virtual
environment.
The operations of methods 700 and/or 800 may collect feedback
information from a user relating to the one or more configurable
boundary parameters and initialize a machine learning component to
learn, adjust, or update the one or more configurable boundary
parameters according to the feedback information.
The present invention may be a system, a method, and/or a computer
program product. The computer program product may include a
computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowcharts and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowcharts and/or
block diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowcharts and/or block diagram block or blocks.
The flowcharts and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowcharts or block diagrams may
represent a module, segment, or portion of instructions, which
comprises one or more executable instructions for implementing the
specified logical function(s). In some alternative implementations,
the functions noted in the block may occur out of the order noted
in the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustrations, and combinations
of blocks in the block diagrams and/or flowchart illustrations, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
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